{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Use Anomaly Detectors in Merlion\n",
    "\n",
    "This notebook will guide you through using all the key features of anomaly detectors in Merlion. Specifically, we will explain\n",
    "\n",
    "1. Initializing an anomaly detection model (including ensembles)\n",
    "1. Training the model\n",
    "1. Producing a series of anomaly scores with the model\n",
    "1. Quantitatively evaluating the model\n",
    "1. Visualizing the model's predictions\n",
    "1. Saving and loading a trained model\n",
    "1. Simulating the live deployment of a model using a `TSADEvaluator`\n",
    "\n",
    "We will be using a single example time series for this whole notebook. We load and visualize it now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.\n",
      "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from merlion.plot import plot_anoms\n",
    "from merlion.utils import TimeSeries\n",
    "from ts_datasets.anomaly import NAB\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "# This is a time series with anomalies in both the train and test split.\n",
    "# time_series and metadata are both time-indexed pandas DataFrames.\n",
    "time_series, metadata = NAB(subset=\"realKnownCause\")[3]\n",
    "\n",
    "# Visualize the full time series\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(time_series)\n",
    "\n",
    "# Label the train/test split with a dashed line & plot anomalies\n",
    "ax.axvline(metadata[metadata.trainval].index[-1], ls=\"--\", lw=2, c=\"k\")\n",
    "plot_anoms(ax, TimeSeries.from_pd(metadata.anomaly))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from merlion.utils import TimeSeries\n",
    "\n",
    "# Get training split\n",
    "train = time_series[metadata.trainval]\n",
    "train_data = TimeSeries.from_pd(train)\n",
    "train_labels = TimeSeries.from_pd(metadata[metadata.trainval].anomaly)\n",
    "\n",
    "# Get testing split\n",
    "test = time_series[~metadata.trainval]\n",
    "test_data = TimeSeries.from_pd(test)\n",
    "test_labels = TimeSeries.from_pd(metadata[~metadata.trainval].anomaly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Initialization\n",
    "\n",
    "In this notebook, we will use three different anomaly detection models:\n",
    "\n",
    "1. Isolation Forest (a classic anomaly detection model)\n",
    "2. WindStats (an in-house model that divides each week into windows of a specified size, and compares time series values to the historical values in the appropriate window)\n",
    "3. Prophet (Facebook's popular forecasting model, adapted for anomaly detection.\n",
    "\n",
    "Let's start by initializing each of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import models & configs\n",
    "from merlion.models.anomaly.isolation_forest import IsolationForest, IsolationForestConfig\n",
    "from merlion.models.anomaly.windstats import WindStats, WindStatsConfig\n",
    "from merlion.models.anomaly.forecast_based.prophet import ProphetDetector, ProphetDetectorConfig\n",
    "\n",
    "# Import a post-rule for thresholding\n",
    "from merlion.post_process.threshold import AggregateAlarms\n",
    "\n",
    "# Import a data processing transform\n",
    "from merlion.transform.moving_average import DifferenceTransform\n",
    "\n",
    "# All models are initialized using the syntax ModelClass(config), where config\n",
    "# is a model-specific configuration object. This is where you specify any\n",
    "# algorithm-specific hyperparameters, any data pre-processing transforms, and\n",
    "# the post-rule you want to use to post-process the anomaly scores (to reduce\n",
    "# noisiness when firing alerts). \n",
    "\n",
    "# We initialize isolation forest using the default config\n",
    "config1 = IsolationForestConfig()\n",
    "model1  = IsolationForest(config1)\n",
    "\n",
    "# We use a WindStats model that splits each week into windows of 60 minutes\n",
    "# each. Anomaly scores in Merlion correspond to z-scores. By default, we would\n",
    "# like to fire an alert for any 4-sigma event, so we specify a threshold rule\n",
    "# which achieves this.\n",
    "config2 = WindStatsConfig(wind_sz=60, threshold=AggregateAlarms(alm_threshold=4))\n",
    "model2  = WindStats(config2)\n",
    "\n",
    "# Prophet is a popular forecasting algorithm. Here, we specify that we would like\n",
    "# to pre-processes the input time series by applying a difference transform,\n",
    "# before running the model on it.\n",
    "config3 = ProphetDetectorConfig(transform=DifferenceTransform())\n",
    "model3  = ProphetDetector(config3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have initialized the individual models, we will also combine them in an ensemble. We set this ensemble's detection threshold to fire alerts for 4-sigma events (the same as WindStats)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from merlion.models.ensemble.anomaly import DetectorEnsemble, DetectorEnsembleConfig\n",
    "\n",
    "ensemble_config = DetectorEnsembleConfig(threshold=AggregateAlarms(alm_threshold=4))\n",
    "ensemble = DetectorEnsemble(config=ensemble_config, models=[model1, model2, model3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "\n",
    "All anomaly detection models (and ensembles) share the same API for training. The `train()` method returns the model's predicted anomaly scores on the training data. Note that you may optionally specify configs that modify the protocol used to train the model's post-rule! You may optionally specify ground truth anomaly labels as well (if you have them), but they are not needed. We give examples of all these behaviors below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training IsolationForest...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:22:24 - cmdstanpy - INFO - Chain [1] start processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training WindStats...\n",
      "\n",
      "Training ProphetDetector...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:22:24 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training ensemble...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:22:26 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:26 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from merlion.evaluate.anomaly import TSADMetric\n",
    "\n",
    "# Train IsolationForest in the default way, using the ground truth anomaly labels\n",
    "# to set the post-rule's threshold\n",
    "print(f\"Training {type(model1).__name__}...\")\n",
    "train_scores_1 = model1.train(train_data=train_data, anomaly_labels=train_labels)\n",
    "\n",
    "# Train WindStats completely unsupervised (this retains our anomaly detection \n",
    "# default anomaly detection threshold of 4)\n",
    "print(f\"\\nTraining {type(model2).__name__}...\")\n",
    "train_scores_2 = model2.train(train_data=train_data, anomaly_labels=None)\n",
    "\n",
    "# Train Prophet with the ground truth anomaly labels, with a post-rule\n",
    "# trained to optimize Precision score\n",
    "print(f\"\\nTraining {type(model3).__name__}...\")\n",
    "post_rule_train_config_3 = dict(metric=TSADMetric.F1)\n",
    "train_scores_3 = model3.train(\n",
    "    train_data=train_data, anomaly_labels=train_labels,\n",
    "    post_rule_train_config=post_rule_train_config_3)\n",
    "\n",
    "# We consider an unsupervised ensemble, which combines the anomaly scores\n",
    "# returned by the models & sets a static anomaly detection threshold of 3.\n",
    "print(\"\\nTraining ensemble...\")\n",
    "ensemble_post_rule_train_config = dict(metric=None)\n",
    "train_scores_e = ensemble.train(\n",
    "    train_data=train_data, anomaly_labels=train_labels,\n",
    "    post_rule_train_config=ensemble_post_rule_train_config,\n",
    ")\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Inference\n",
    "\n",
    "There are two ways to invoke an anomaly detection model: `model.get_anomaly_score()` returns the model's raw anomaly scores, while `model.get_anomaly_label()` returns the model's post-processed anomaly scores. The post-processing calibrates the anomaly scores to be interpretable as z-scores, and it also sparsifies them such that any nonzero values should be treated as an alert that a particular timestamp is anomalous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsolationForest.get_anomaly_score() nonzero values (raw)\n",
      "                     anom_score\n",
      "time                           \n",
      "2013-12-14 16:55:00    0.424103\n",
      "2013-12-14 17:00:00    0.418938\n",
      "2013-12-14 17:05:00    0.484891\n",
      "2013-12-14 17:10:00    0.500257\n",
      "2013-12-14 17:15:00    0.449213\n",
      "...                         ...\n",
      "2014-02-19 15:05:00    0.419456\n",
      "2014-02-19 15:10:00    0.415807\n",
      "2014-02-19 15:15:00    0.406724\n",
      "2014-02-19 15:20:00    0.427094\n",
      "2014-02-19 15:25:00    0.428348\n",
      "\n",
      "[19279 rows x 1 columns]\n",
      "\n",
      "IsolationForest.get_anomaly_label() nonzero values (post-processed)\n",
      "                     anom_score\n",
      "time                           \n",
      "2013-12-16 16:00:00    3.251397\n",
      "2013-12-16 18:35:00    3.681691\n",
      "2013-12-27 19:25:00    3.914430\n",
      "2013-12-27 23:20:00    3.260543\n",
      "2013-12-28 04:15:00    3.738462\n",
      "2013-12-28 06:20:00    3.303482\n",
      "2014-01-02 10:00:00    3.233514\n",
      "2014-01-05 17:50:00    3.791805\n",
      "2014-01-12 09:25:00    3.535895\n",
      "2014-01-13 10:05:00    3.314500\n",
      "2014-01-16 12:50:00    3.850349\n",
      "2014-01-24 12:50:00    4.170855\n",
      "2014-01-27 17:45:00    3.537919\n",
      "2014-01-28 22:00:00    3.451974\n",
      "2014-01-30 23:40:00    3.550075\n",
      "2014-02-02 23:45:00    3.359105\n",
      "2014-02-03 11:55:00    4.175556\n",
      "2014-02-05 05:10:00    3.675433\n",
      "2014-02-09 11:55:00    4.005116\n",
      "2014-02-13 19:15:00    3.247573\n",
      "\n",
      "IsolationForest fires 20 alarms\n",
      "\n",
      "Raw scores at the locations where alarms were fired:\n",
      "                     anom_score\n",
      "time                           \n",
      "2013-12-16 16:00:00    0.701491\n",
      "2013-12-16 18:35:00    0.772563\n",
      "2013-12-27 19:25:00    0.810997\n",
      "2013-12-27 23:20:00    0.702972\n",
      "2013-12-28 04:15:00    0.781997\n",
      "2013-12-28 06:20:00    0.709952\n",
      "2014-01-02 10:00:00    0.698602\n",
      "2014-01-05 17:50:00    0.790835\n",
      "2014-01-12 09:25:00    0.748293\n",
      "2014-01-13 10:05:00    0.711750\n",
      "2014-01-16 12:50:00    0.800493\n",
      "2014-01-24 12:50:00    0.852493\n",
      "2014-01-27 17:45:00    0.748630\n",
      "2014-01-28 22:00:00    0.734366\n",
      "2014-01-30 23:40:00    0.750652\n",
      "2014-02-02 23:45:00    0.719052\n",
      "2014-02-03 11:55:00    0.853260\n",
      "2014-02-05 05:10:00    0.771522\n",
      "2014-02-09 11:55:00    0.825713\n",
      "2014-02-13 19:15:00    0.700873\n",
      "Post-processed scores are interpretable as z-scores\n",
      "Raw scores are challenging to interpret\n"
     ]
    }
   ],
   "source": [
    "# Here is a full example for the first model, IsolationForest\n",
    "scores_1 = model1.get_anomaly_score(test_data)\n",
    "scores_1_df = scores_1.to_pd()\n",
    "print(f\"{type(model1).__name__}.get_anomaly_score() nonzero values (raw)\")\n",
    "print(scores_1_df[scores_1_df.iloc[:, 0] != 0])\n",
    "print()\n",
    "\n",
    "labels_1 = model1.get_anomaly_label(test_data)\n",
    "labels_1_df = labels_1.to_pd()\n",
    "print(f\"{type(model1).__name__}.get_anomaly_label() nonzero values (post-processed)\")\n",
    "print(labels_1_df[labels_1_df.iloc[:, 0] != 0])\n",
    "print()\n",
    "\n",
    "print(f\"{type(model1).__name__} fires {(labels_1_df.values != 0).sum()} alarms\")\n",
    "print()\n",
    "\n",
    "print(\"Raw scores at the locations where alarms were fired:\")\n",
    "print(scores_1_df[labels_1_df.iloc[:, 0] != 0])\n",
    "print(\"Post-processed scores are interpretable as z-scores\")\n",
    "print(\"Raw scores are challenging to interpret\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same API is shared for all models, including ensembles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_2 = model2.get_anomaly_score(test_data)\n",
    "labels_2 = model2.get_anomaly_label(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_3 = model3.get_anomaly_score(test_data)\n",
    "labels_3 = model3.get_anomaly_label(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_e = ensemble.get_anomaly_score(test_data)\n",
    "labels_e = ensemble.get_anomaly_label(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantitative Evaluation\n",
    "\n",
    "It is fairly transparent to visualize a model's predicted anomaly scores and also quantitatively evaluate its anomaly labels. For evaluation, we use specialized definitions of precision, recall, and F1 as revised point-adjusted metrics (see the technical report for more details). We also consider the mean time to detect anomalies.\n",
    "\n",
    "In general, you may use the `TSADMetric` enum to compute evaluation metrics for a time series using the syntax\n",
    "```\n",
    "TSADMetric.<metric_name>.value(ground_truth=ground_truth, predict=anomaly_labels)\n",
    "```\n",
    "where `<metric_name>` is the name of the evaluation metric (see the API docs for details and more options), `ground_truth` is a time series of ground truth anomaly labels, and `anomaly_labels` is the output of `model.get_anomaly_label()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsolationForest\n",
      "Precision: 0.1667\n",
      "Recall:    1.0000\n",
      "F1:        0.2857\n",
      "MTTD:      0 days 23:31:40\n",
      "\n",
      "WindStats\n",
      "Precision: 0.0270\n",
      "Recall:    1.0000\n",
      "F1:        0.0526\n",
      "MTTD:      0 days 12:01:40\n",
      "\n",
      "ProphetDetector\n",
      "Precision: 0.2000\n",
      "Recall:    0.6667\n",
      "F1:        0.3077\n",
      "MTTD:      1 days 10:22:30\n",
      "\n",
      "DetectorEnsemble\n",
      "Precision: 0.4000\n",
      "Recall:    0.6667\n",
      "F1:        0.5000\n",
      "MTTD:      1 days 10:22:30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from merlion.evaluate.anomaly import TSADMetric\n",
    "\n",
    "for model, labels in [(model1, labels_1), (model2, labels_2), (model3, labels_3), (ensemble, labels_e)]:\n",
    "    print(f\"{type(model).__name__}\")\n",
    "    precision = TSADMetric.Precision.value(ground_truth=test_labels, predict=labels)\n",
    "    recall = TSADMetric.Recall.value(ground_truth=test_labels, predict=labels)\n",
    "    f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=labels)\n",
    "    mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=labels)\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall:    {recall:.4f}\")\n",
    "    print(f\"F1:        {f1:.4f}\")\n",
    "    print(f\"MTTD:      {mttd}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the individual models are trained to optimize F1 directly, they all have low precision, high recall, and a mean time to detect of around 1 day. However, by instead training the individual models to optimize precision, and training a model combination unit to optimize F1, we are able to greatly increase the precision and F1 score, at the cost of a lower recall and higher mean time to detect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Visualization\n",
    "\n",
    "Let's now visualize the model predictions that led to these outcomes. The option `filter_scores=True` means that we want to plot the post-processed anomaly scores (i.e. returned by `model.get_anomaly_label()`). You may instead specify `filter_scores=False` to visualize the raw anomaly scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsolationForest\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WindStats\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ProphetDetector\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for model in [model1, model2, model3]:\n",
    "    print(type(model).__name__)\n",
    "    fig, ax = model.plot_anomaly(\n",
    "        time_series=test_data, time_series_prev=train_data,\n",
    "        filter_scores=True, plot_time_series_prev=True)\n",
    "    plot_anoms(ax=ax, anomaly_labels=test_labels)\n",
    "    plt.show()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So all the individual models generate quite a few false positives. Let's see how the ensemble does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = ensemble.plot_anomaly(\n",
    "    time_series=test_data, time_series_prev=train_data,\n",
    "    filter_scores=True, plot_time_series_prev=True)\n",
    "plot_anoms(ax=ax, anomaly_labels=test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the ensemble misses one of the three anomalies in the test split, but it also greatly reduces the number of false positives relative to the other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving & Loading Models\n",
    "\n",
    "All models have a `save()` method and `load()` class method. Models may also be loaded with the assistance of the `ModelFactory`, which works for arbitrary models. The `save()` method creates a new directory at the specified path, where it saves a `json` file representing the model's config, as well as a binary file for the model's state.\n",
    "\n",
    "We will demonstrate these behaviors using our `IsolationForest` model (`model1`) for concreteness. Note that the config explicitly tracks the transform (to pre-process the data), the calibrator (to transform raw anomaly scores into z-scores), the thresholding rule (to sparsify the calibrated anomaly scores)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsolationForest Config\n",
      "{'calibrator': {'abs_score': True,\n",
      "                'anchors': [[0.38992633996347176, 0.0],\n",
      "                            [0.4187750781361715, 0.5],\n",
      "                            [0.445336977389891, 1.0],\n",
      "                            [0.47974261897360404, 1.5],\n",
      "                            [0.5271631189090943, 2.0],\n",
      "                            [0.8301789920204418, 4.032894437734716],\n",
      "                            [1.0, 5.032894437734716]],\n",
      "                'max_score': 1.0,\n",
      "                'name': 'AnomScoreCalibrator'},\n",
      " 'dim': 1,\n",
      " 'enable_calibrator': True,\n",
      " 'enable_threshold': True,\n",
      " 'max_n_samples': 1.0,\n",
      " 'n_estimators': 100,\n",
      " 'threshold': {'abs_score': True,\n",
      "               'alm_suppress_minutes': 120,\n",
      "               'alm_threshold': 3.2263155501877727,\n",
      "               'alm_window_minutes': 60,\n",
      "               'min_alm_in_window': 2,\n",
      "               'name': 'AggregateAlarms'},\n",
      " 'transform': {'name': 'TransformSequence',\n",
      "               'transforms': [{'name': 'DifferenceTransform'},\n",
      "                              {'multivar_skip': True,\n",
      "                               'name': 'Shingle',\n",
      "                               'size': 2,\n",
      "                               'stride': 1}]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import pprint\n",
    "from merlion.models.factory import ModelFactory\n",
    "\n",
    "# Save the model\n",
    "os.makedirs(\"models\", exist_ok=True)\n",
    "path = os.path.join(\"models\", \"isf\")\n",
    "model1.save(path)\n",
    "\n",
    "# Print the config saved\n",
    "pp = pprint.PrettyPrinter()\n",
    "with open(os.path.join(path, \"config.json\")) as f:\n",
    "    print(f\"{type(model1).__name__} Config\")\n",
    "    pp.pprint(json.load(f))\n",
    "\n",
    "# Load the model using Prophet.load()\n",
    "model2_loaded = IsolationForest.load(dirname=path)\n",
    "\n",
    "# Load the model using the ModelFactory\n",
    "model2_factory_loaded = ModelFactory.load(name=\"IsolationForest\", model_path=path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same exact thing with ensembles! Note that the ensemble stores its underlying models in a nested structure. This is all reflected in the config."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Config\n",
      "{'calibrator': {'abs_score': True,\n",
      "                'anchors': None,\n",
      "                'max_score': 1000,\n",
      "                'name': 'AnomScoreCalibrator'},\n",
      " 'combiner': {'_override_models_used': {},\n",
      "              'abs_score': True,\n",
      "              'n_models': 3,\n",
      "              'name': 'Mean'},\n",
      " 'dim': 1,\n",
      " 'enable_calibrator': False,\n",
      " 'enable_threshold': True,\n",
      " 'models': [{'calibrator': {'abs_score': True,\n",
      "                            'anchors': [[0.38992633996347176, 0.0],\n",
      "                                        [0.4187750781361715, 0.5],\n",
      "                                        [0.445336977389891, 1.0],\n",
      "                                        [0.47974261897360404, 1.5],\n",
      "                                        [0.5271631189090943, 2.0],\n",
      "                                        [0.8301789920204418, 4.032894437734716],\n",
      "                                        [1.0, 5.032894437734716]],\n",
      "                            'max_score': 1.0,\n",
      "                            'name': 'AnomScoreCalibrator'},\n",
      "             'dim': 1,\n",
      "             'enable_calibrator': True,\n",
      "             'enable_threshold': False,\n",
      "             'max_n_samples': 1.0,\n",
      "             'n_estimators': 100,\n",
      "             'name': 'IsolationForest',\n",
      "             'threshold': {'abs_score': True,\n",
      "                           'alm_suppress_minutes': 120,\n",
      "                           'alm_threshold': 3.0,\n",
      "                           'alm_window_minutes': 60,\n",
      "                           'min_alm_in_window': 2,\n",
      "                           'name': 'AggregateAlarms'},\n",
      "             'transform': {'name': 'TransformSequence',\n",
      "                           'transforms': [{'name': 'DifferenceTransform'},\n",
      "                                          {'multivar_skip': True,\n",
      "                                           'name': 'Shingle',\n",
      "                                           'size': 2,\n",
      "                                           'stride': 1}]}},\n",
      "            {'calibrator': {'abs_score': True,\n",
      "                            'anchors': [[0.0004858784421674658, 0.0],\n",
      "                                        [0.4318659926885851, 0.5],\n",
      "                                        [0.9774407588312237, 1.0],\n",
      "                                        [1.4231054875246496, 1.5],\n",
      "                                        [1.7393725195754337, 2.0],\n",
      "                                        [2.4271291767175622, 4.032894437734716],\n",
      "                                        [4.8542583534351245,\n",
      "                                         5.032894437734716]],\n",
      "                            'max_score': 1000,\n",
      "                            'name': 'AnomScoreCalibrator'},\n",
      "             'dim': 1,\n",
      "             'enable_calibrator': True,\n",
      "             'enable_threshold': False,\n",
      "             'max_day': 4,\n",
      "             'name': 'WindStats',\n",
      "             'threshold': {'abs_score': True,\n",
      "                           'alm_suppress_minutes': 120,\n",
      "                           'alm_threshold': 4,\n",
      "                           'alm_window_minutes': 60,\n",
      "                           'min_alm_in_window': 2,\n",
      "                           'name': 'AggregateAlarms'},\n",
      "             'transform': {'name': 'DifferenceTransform'},\n",
      "             'wind_sz': 60},\n",
      "            {'calibrator': {'abs_score': True,\n",
      "                            'anchors': [[0.00040425650796231867, 0.0],\n",
      "                                        [0.5103916318368437, 0.5],\n",
      "                                        [1.0369977090370754, 1.0],\n",
      "                                        [1.5325298959635636, 1.5],\n",
      "                                        [1.9215534761800885, 2.0],\n",
      "                                        [5.2965340676146635, 4.032894437734716],\n",
      "                                        [10.593068135229327,\n",
      "                                         5.032894437734716]],\n",
      "                            'max_score': 1000,\n",
      "                            'name': 'AnomScoreCalibrator'},\n",
      "             'daily_seasonality': 'auto',\n",
      "             'dim': 1,\n",
      "             'enable_calibrator': True,\n",
      "             'enable_threshold': False,\n",
      "             'exog_aggregation_policy': 'Mean',\n",
      "             'exog_missing_value_policy': 'ZFill',\n",
      "             'exog_transform': {'bias': None,\n",
      "                                'name': 'MeanVarNormalize',\n",
      "                                'normalize_bias': True,\n",
      "                                'normalize_scale': True,\n",
      "                                'scale': None},\n",
      "             'holidays': None,\n",
      "             'invert_transform': False,\n",
      "             'max_forecast_steps': None,\n",
      "             'name': 'ProphetDetector',\n",
      "             'seasonality_mode': 'additive',\n",
      "             'target_seq_index': 0,\n",
      "             'threshold': {'abs_score': True,\n",
      "                           'alm_suppress_minutes': 120,\n",
      "                           'alm_threshold': 3,\n",
      "                           'alm_window_minutes': 60,\n",
      "                           'min_alm_in_window': 2,\n",
      "                           'name': 'AggregateAlarms'},\n",
      "             'transform': {'name': 'DifferenceTransform'},\n",
      "             'uncertainty_samples': 100,\n",
      "             'weekly_seasonality': 'auto',\n",
      "             'yearly_seasonality': 'auto'}],\n",
      " 'threshold': {'abs_score': True,\n",
      "               'alm_suppress_minutes': 120,\n",
      "               'alm_threshold': 4,\n",
      "               'alm_window_minutes': 60,\n",
      "               'min_alm_in_window': 2,\n",
      "               'name': 'AggregateAlarms'},\n",
      " 'transform': {'name': 'Identity'}}\n"
     ]
    }
   ],
   "source": [
    "# Save the ensemble\n",
    "path = os.path.join(\"models\", \"ensemble\")\n",
    "ensemble.save(path)\n",
    "\n",
    "# Print the config saved. Note that we've saved all individual models,\n",
    "# and their paths are specified under the model_paths key.\n",
    "pp = pprint.PrettyPrinter()\n",
    "with open(os.path.join(path, \"config.json\")) as f:\n",
    "    print(f\"Ensemble Config\")\n",
    "    pp.pprint(json.load(f))\n",
    "\n",
    "# Load the selector\n",
    "selector_loaded = DetectorEnsemble.load(dirname=path)\n",
    "\n",
    "# Load the selector using the ModelFactory\n",
    "selector_factory_loaded = ModelFactory.load(name=\"DetectorEnsemble\", model_path=path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating Live Model Deployment\n",
    "\n",
    "A typical model deployment scenario is as follows:\n",
    "1. Train an initial model on some recent historical data, optionally with labels.\n",
    "1. At a regular interval `retrain_freq` (e.g. once per week), retrain the entire model unsupervised (i.e. with no labels) on the most recent data.\n",
    "1. Obtain the model's predicted anomaly scores for the time series values that occur between re-trainings. We perform this operation in batch, but a deployment scenario may do this in streaming.\n",
    "1. Optionally, specify a maximum amount of data (`train_window`) that the model should use for training (e.g. the most recent 2 weeks of data).\n",
    "\n",
    "We provide a `TSADEvaluator` object which simulates the above deployment scenario, and also allows a user to evaluate the quality of the forecaster according to an evaluation metric of their choice. We illustrate an example below using the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the evaluator\n",
    "from merlion.evaluate.anomaly import TSADEvaluator, TSADEvaluatorConfig\n",
    "\n",
    "evaluator = TSADEvaluator(model=ensemble, config=TSADEvaluatorConfig(retrain_freq=\"7d\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:22:42 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:43 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  10%|█         | 604800/5783700 [00:00<00:03, 1307785.29it/s]17:22:44 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:44 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  21%|██        | 1209600/5783700 [00:02<00:08, 551941.29it/s]17:22:46 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:46 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  31%|███▏      | 1814400/5783700 [00:04<00:10, 365146.12it/s]17:22:48 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:48 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  42%|████▏     | 2419200/5783700 [00:06<00:09, 339456.87it/s]17:22:50 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:51 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  52%|█████▏    | 3024000/5783700 [00:08<00:09, 291111.79it/s]17:22:53 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:53 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  63%|██████▎   | 3628800/5783700 [00:11<00:07, 269495.41it/s]17:22:55 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:22:58 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  73%|███████▎  | 4233600/5783700 [00:15<00:07, 206629.47it/s]17:23:00 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:23:01 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  84%|████████▎ | 4838400/5783700 [00:19<00:05, 188649.53it/s]17:23:04 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:23:06 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator:  94%|█████████▍| 5443200/5783700 [00:24<00:02, 166964.38it/s]17:23:09 - cmdstanpy - INFO - Chain [1] start processing\n",
      "17:23:12 - cmdstanpy - INFO - Chain [1] done processing\n",
      "TSADEvaluator: 100%|██████████| 5783700/5783700 [00:29<00:00, 193604.33it/s]\n"
     ]
    }
   ],
   "source": [
    "# The kwargs we would provide to ensemble.train() for the initial training\n",
    "# Note that we are training the ensemble unsupervised.\n",
    "train_kwargs = {\"anomaly_labels\": None}\n",
    "\n",
    "# We will use the default kwargs for re-training (these leave the\n",
    "# post-rules unchanged, since there are no new labels)\n",
    "retrain_kwargs = None\n",
    "\n",
    "# We call evaluator.get_predict() to get the time series of anomaly scores\n",
    "# produced by the anomaly detector when deployed in this manner\n",
    "train_scores, test_scores = evaluator.get_predict(\n",
    "    train_vals=train_data, test_vals=test_data,\n",
    "    train_kwargs=train_kwargs, retrain_kwargs=retrain_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Performance\n",
      "Precision: 0.5000\n",
      "Recall:    0.6667\n",
      "F1:        0.5714\n",
      "MTTD:      1 days 10:25:00\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Now let's evaluate how we did.\n",
    "precision = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Precision)\n",
    "recall    = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Recall)\n",
    "f1        = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.F1)\n",
    "mttd      = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.MeanTimeToDetect)\n",
    "print(\"Ensemble Performance\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall:    {recall:.4f}\")\n",
    "print(f\"F1:        {f1:.4f}\")\n",
    "print(f\"MTTD:      {mttd}\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we see that by simply re-training the ensemble weekly in an unsupervised manner, we have increased the precision from $\\frac{2}{5}$ to $\\frac{2}{4}$, while leaving unchanged the recall and mean time to detect. This is due to data drift over time."
   ]
  }
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